Static user equipment geolocation

ABSTRACT

The described technology is generally directed towards user equipment geolocation. Network measurement data associated with user equipment can be separated into static periods in which the user equipment was not moving, and moving periods in which the user equipment was moving. Static location processing can be applied to determine static locations from the static period network measurements, and moving location processing can be applied to determine moving locations from the moving period network measurements. Resulting static location information and moving location information can then be merged in order to improve the accuracy of both the static and the moving location information. The enhanced accuracy location information can be stored and used for any desired application.

TECHNICAL FIELD

The subject application is related to user equipment geolocation bycellular communication systems, for example, geolocation of userequipment that communicate via fourth generation (4G), fifth generation(5G), and subsequent generation cellular networks.

BACKGROUND

User equipment (UE) geolocation is a key enabler for radio accessnetwork (RAN) optimization and network planning. Network serviceproviders can identify coverage holes, hot spots and capacity issues bycombining UE location information with wireless network measurementssuch as signal strength and UE throughput. This enables providers tobetter plan network capacity, resulting in improved network performance.High quality UE location estimation is important for network planning.

Most existing UE geolocation techniques are based on wireless networkmeasurements collected from cells instantly or in a very short timewindow, e.g., over several minutes. It is challenging to estimate UElocations accurately with a limited quantity of network measurements,especially for stationary UEs. Due to the high variability of networkmeasurements collected for UEs, location estimates usually containerrors. Moreover, UE route estimates generated by connecting suchlocation estimates can be improbable or illogical.

In an example, geotagging techniques can estimate UE locations based oninstantaneous network measurements or a short history of measurements.In these methods, the time span of historical measurements is typicallyvery short due to limited computational resources. Results often havepoor location estimation accuracy, especially when there is significantnoise in the measurements. It is common for a UE to generatemeasurements in which the observed serving cells and correspondingtiming advance (TA), reference signal received power (RSRP) andreference signal received quality (RSRQ) values vary significantly.Because the measurements vary, the resulting location estimates alsovary.

The above-described background is merely intended to provide acontextual overview of some current issues, and is not intended to beexhaustive. Other contextual information may become further apparentupon review of the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the subject disclosureare described with reference to the following figures, wherein likereference numerals refer to like parts throughout the various viewsunless otherwise specified.

FIG. 1 illustrates an example wireless communication system, inaccordance with various aspects and embodiments of the subjectdisclosure.

FIG. 2 illustrates example static and moving locations of userequipment, and example network measurements reported to network nodes ofa wireless communication system, in accordance with various aspects andembodiments of the subject disclosure.

FIG. 3 illustrates example network equipment configured to performmobility mode identification of static and moving user equipmentmobility modes, in accordance with various aspects and embodiments ofthe subject disclosure.

FIG. 4 is a flow diagram representing example operations of networkequipment to perform mobility mode identification of static and movinguser equipment mobility modes, in accordance with various aspects andembodiments of the subject disclosure.

FIG. 5 is a flow diagram representing example operations of networkequipment to prepare a time series of historical geotagged call tracedata, in accordance with various aspects and embodiments of the subjectdisclosure.

FIG. 6 is a flow diagram representing example operations of networkequipment to apply mobility mode identification, in accordance withvarious aspects and embodiments of the subject disclosure.

FIG. 7 is a flow diagram representing example operations of networkequipment to estimate UE routes, in accordance with various aspects andembodiments of the subject disclosure.

FIG. 8 illustrates example network equipment configured to estimatestatic user equipment locations based on network measurement data, inaccordance with various aspects and embodiments of the subjectdisclosure.

FIG. 9 is a flow diagram representing example operations of networkequipment to estimate static user equipment locations based on networkmeasurement data, in accordance with various aspects and embodiments ofthe subject disclosure.

FIG. 10 illustrates example recurring network measurement data that canbe used to identify static user equipment, in accordance with variousaspects and embodiments of the subject disclosure.

FIG. 11 is a flow diagram representing example operations of networkequipment to perform a static recurrent pattern identification process,in accordance with various aspects and embodiments of the subjectdisclosure.

FIG. 12 is a flow diagram representing example operations of networkequipment to perform a static geolocation process, in accordance withvarious aspects and embodiments of the subject disclosure.

FIG. 13 illustrates example network equipment configured to estimatemoving user equipment locations based on network measurement data, inaccordance with various aspects and embodiments of the subjectdisclosure.

FIG. 14 is a flow diagram representing example operations of networkequipment to estimate moving user equipment locations based on networkmeasurement data, in accordance with various aspects and embodiments ofthe subject disclosure.

FIG. 15 is a flow diagram representing example operations of networkequipment to perform moving smoothing adjustments of estimatedlocations, in accordance with various aspects and embodiments of thesubject disclosure

FIG. 16 is a flow diagram representing example operations of networkequipment to assign a static location as an estimated user equipmentlocation during a static period, in accordance with various aspects andembodiments of the subject disclosure.

FIG. 17 is a flow diagram representing example operations of networkequipment to reclassify a user equipment moving period as a static,static period, and assigning a static location to the reclassifiedperiod, in accordance with various aspects and embodiments of thesubject disclosure.

FIG. 18 is a flow diagram representing example operations of networkequipment to assign a static location as an estimated user equipmentlocation during a static period based on reliability scores of estimatedlocation information pertaining to the static period, in accordance withvarious aspects and embodiments of the subject disclosure.

FIG. 19 is a block diagram of an example computer that can be operableto execute processes and methods in accordance with various aspects andembodiments of the subject disclosure.

DETAILED DESCRIPTION

One or more embodiments are now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the various embodiments. It is evident,however, that the various embodiments can be practiced without thesespecific details, and without applying to any particular networkedenvironment or standard.

One or more aspects of the technology described herein are generallydirected towards user equipment geolocation, i.e., identifying physicallocations at which user equipment is or was located. Network measurementdata associated with user equipment can be separated into static periodsin which the user equipment was not moving, and moving periods in whichthe user equipment was moving. Static location processing can be appliedto determine static locations from the static period networkmeasurements, and moving location processing can be applied to determinemoving locations from the moving period network measurements. Resultingstatic location information and moving location information can then bemerged in order to improve the accuracy of both the static and themoving location information. The enhanced accuracy location informationcan be stored and used for any desired application. Further aspects andembodiments of this disclosure are described in detail below.

As used in this disclosure, in some embodiments, the terms “component,”“system” and the like are intended to refer to, or comprise, acomputer-related entity or an entity related to an operational apparatuswith one or more specific functionalities, wherein the entity can beeither hardware, a combination of hardware and software, software, orsoftware in execution. As an example, a component can be, but is notlimited to being, a process running on a processor, a processor, anobject, an executable, a thread of execution, computer-executableinstructions, a program, and/or a computer. By way of illustration andnot limitation, both an application running on a server and the servercan be a component.

One or more components can reside within a process and/or thread ofexecution and a component can be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components can communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which is operated by a software application orfirmware application executed by a processor, wherein the processor canbe internal or external to the apparatus and executes at least a part ofthe software or firmware application. As yet another example, acomponent can be an apparatus that provides specific functionalitythrough electronic components without mechanical parts, the electroniccomponents can comprise a processor therein to execute software orfirmware that confers at least in part the functionality of theelectronic components. While various components have been illustrated asseparate components, it will be appreciated that multiple components canbe implemented as a single component, or a single component can beimplemented as multiple components, without departing from exampleembodiments.

The term “facilitate” as used herein is in the context of a system,device or component “facilitating” one or more actions or operations, inrespect of the nature of complex computing environments in whichmultiple components and/or multiple devices can be involved in somecomputing operations. Non-limiting examples of actions that may or maynot involve multiple components and/or multiple devices comprisetransmitting or receiving data, establishing a connection betweendevices, determining intermediate results toward obtaining a result,etc. In this regard, a computing device or component can facilitate anoperation by playing any part in accomplishing the operation. Whenoperations of a component are described herein, it is thus to beunderstood that where the operations are described as facilitated by thecomponent, the operations can be optionally completed with thecooperation of one or more other computing devices or components, suchas, but not limited to, sensors, antennae, audio and/or visual outputdevices, other devices, etc.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable (or machine-readable) device or computer-readable (ormachine-readable) storage/communications media. For example, computerreadable storage media can comprise, but are not limited to, magneticstorage devices (e.g., hard disk, floppy disk, magnetic strips), opticaldisks (e.g., compact disk (CD), digital versatile disk (DVD)), smartcards, and flash memory devices (e.g., card, stick, key drive). Ofcourse, those skilled in the art will recognize many modifications canbe made to this configuration without departing from the scope or spiritof the various embodiments.

Moreover, terms such as “mobile device equipment,” “mobile station,”“mobile,” “subscriber station,” “access terminal,” “terminal,”“handset,” “communication device,” “mobile device” (and/or termsrepresenting similar terminology) can refer to a wireless deviceutilized by a subscriber or mobile device of a wireless communicationservice to receive or convey data, control, voice, video, sound, gamingor substantially any data-stream or signaling-stream. The foregoingterms are utilized interchangeably herein and with reference to therelated drawings. Likewise, the terms “access point (AP),” “Base Station(BS),” “BS transceiver,” “BS device,” “cell site,” “cell site device,”“gNode B (gNB),” “evolved Node B (eNode B, eNB),” “home Node B (HNB)”and the like, refer to wireless network components or appliances thattransmit and/or receive data, control, voice, video, sound, gaming orsubstantially any data-stream or signaling-stream from one or moresubscriber stations. Data and signaling streams can be packetized orframe-based flows.

Furthermore, the terms “device,” “communication device,” “mobiledevice,” “subscriber,” “customer entity,” “consumer,” “customer entity,”“entity” and the like are employed interchangeably throughout, unlesscontext warrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based on complex mathematical formalisms), which canprovide simulated vision, sound recognition and so forth.

It should be noted that although various aspects and embodiments havebeen described herein in the context of 4G, 5G, or other next generationnetworks, the disclosed aspects are not limited to a 4G or 5Gimplementation, and/or other network next generation implementations, asthe techniques can also be applied, for example, in third generation(3G), or other 4G systems. In this regard, aspects or features of thedisclosed embodiments can be exploited in substantially any wirelesscommunication technology. Such wireless communication technologies caninclude universal mobile telecommunications system (UMTS), global systemfor mobile communication (GSM), code division multiple access (CDMA),wideband CDMA (WCMDA), CDMA2000, time division multiple access (TDMA),frequency division multiple access (FDMA), multi-carrier CDMA (MC-CDMA),single-carrier CDMA (SC-CDMA), single-carrier FDMA (SC-FDMA), orthogonalfrequency division multiplexing (OFDM), discrete Fourier transformspread OFDM (DFT-spread OFDM), single carrier FDMA (SC-FDMA), filterbank based multi-carrier (FBMC), zero tail DFT-spread-OFDM (ZTDFT-s-OFDM), generalized frequency division multiplexing (GFDM), fixedmobile convergence (FMC), universal fixed mobile convergence (UFMC),unique word OFDM (UW-OFDM), unique word DFT-spread OFDM (UWDFT-Spread-OFDM), cyclic prefix OFDM (CP-OFDM), resource-block-filteredOFDM, wireless fidelity (Wi-Fi), worldwide interoperability formicrowave access (WiMAX), wireless local area network (WLAN), generalpacket radio service (GPRS), enhanced GPRS, third generation partnershipproject (3GPP), long term evolution (LTE), LTE frequency division duplex(FDD), time division duplex (TDD), 5G, third generation partnershipproject 2 (3GPP2), ultra mobile broadband (UMB), high speed packetaccess (HSPA), evolved high speed packet access (HSPA+), high-speeddownlink packet access (HSDPA), high-speed uplink packet access (HSUPA),Zigbee, or another institute of electrical and electronics engineers(IEEE) 802.12 technology. In this regard, all or substantially allaspects disclosed herein can be exploited in legacy telecommunicationtechnologies.

FIG. 1 illustrates a non-limiting example of a wireless communicationsystem 100 which can be used in connection with at least someembodiments of the subject disclosure. In one or more embodiments,system 100 can comprise one or more user equipment UEs 102 ₁, 102 ₂,referred to collectively as UEs 102, a network node 104 that supportscellular communications in a service area 110, also known as a cell, andcommunication service provider network(s) 106.

The non-limiting term “user equipment” can refer to any type of devicethat can communicate with a network node 104 in a cellular or mobilecommunication system 100. UEs 102 can have one or more antenna panelshaving vertical and horizontal elements. Examples of UEs 102 comprisetarget devices, device to device (D2D) UEs, machine type UEs or UEscapable of machine to machine (M2M) communications, personal digitalassistants (PDAs), tablets, mobile terminals, smart phones, laptopmounted equipment (LME), universal serial bus (USB) dongles enabled formobile communications, computers having mobile capabilities, mobiledevices such as cellular phones, laptops having laptop embeddedequipment (LEE, such as a mobile broadband adapter), tablet computershaving mobile broadband adapters, wearable devices, virtual reality (VR)devices, heads-up display (HUD) devices, smart cars, machine-typecommunication (MTC) devices, augmented reality head mounted displays,and the like. UEs 102 can also comprise IOT devices that communicatewirelessly.

In various embodiments, system 100 comprises communication serviceprovider network(s) 106 serviced by one or more wireless communicationnetwork providers. Communication service provider network(s) 106 cancomprise a “core network”. In example embodiments, UEs 102 can becommunicatively coupled to the communication service provider network(s)106 via network node 104. The network node 104 (e.g., network nodedevice) can communicate with UEs 102, thus providing connectivitybetween the UEs 102 and the wider cellular network. The UEs 102 can sendtransmission type recommendation data to the network node 104. Thetransmission type recommendation data can comprise a recommendation totransmit data via a closed loop multiple input multiple output (MIMO)mode and/or a rank-1 precoder mode.

A network node 104 can have a cabinet and other protected enclosures,computing devices, an antenna mast, and multiple antennas for performingvarious transmission operations (e.g., MIMO operations) and fordirecting/steering signal beams. Network node 104 can comprise one ormore base station devices which implement features of the network node104. Network nodes can serve several cells, depending on theconfiguration and type of antenna. In example embodiments, UEs 102 cansend and/or receive communication data via a wireless link to thenetwork node 104. The dashed arrow lines from the network node 104 tothe UEs 102 represent downlink (DL) communications to the UEs 102. Thesolid arrow lines from the UEs 102 to the network node 104 representuplink (UL) communications.

Communication service provider networks 106 can facilitate providingwireless communication services to UEs 102 via the network node 104and/or various additional network devices (not shown) included in theone or more communication service provider networks 106. The one or morecommunication service provider networks 106 can comprise various typesof disparate networks, including but not limited to: cellular networks,femto networks, picocell networks, microcell networks, internet protocol(IP) networks Wi-Fi service networks, broadband service network,enterprise networks, cloud based networks, millimeter wave networks andthe like. For example, in at least one implementation, system 100 can beor comprise a large scale wireless communication network that spansvarious geographic areas. According to this implementation, the one ormore communication service provider networks 106 can be or comprise thewireless communication network and/or various additional devices andcomponents of the wireless communication network (e.g., additionalnetwork devices and cell, additional UEs, network server devices, etc.).

The network node 104 can be connected to the one or more communicationservice provider networks 106 via one or more backhaul links 108. Forexample, the one or more backhaul links 108 can comprise wired linkcomponents, such as a T1/E1 phone line, a digital subscriber line (DSL)(e.g., either synchronous or asynchronous), an asymmetric DSL (ADSL), anoptical fiber backbone, a coaxial cable, and the like. The one or morebackhaul links 108 can also comprise wireless link components, such asbut not limited to, line-of-sight (LOS) or non-LOS links which cancomprise terrestrial air-interfaces or deep space links (e.g., satellitecommunication links for navigation). Backhaul links 108 can beimplemented via a “transport network” in some embodiments. In anotherembodiment, network node 104 can be part of an integrated access andbackhaul network. This may allow easier deployment of a dense network ofself-backhauled 5G cells in a more integrated manner by building uponmany of the control and data channels/procedures defined for providingaccess to UEs.

Wireless communication system 100 can employ various cellular systems,technologies, and modulation modes to facilitate wireless radiocommunications between devices (e.g., the UE 102 and the network node104). While example embodiments might be described for 5G new radio (NR)systems, the embodiments can be applicable to any radio accesstechnology (RAT) or multi-RAT system where the UE operates usingmultiple carriers, e.g., LTE FDD/TDD, GSM/GERAN, CDMA2000 etc.

For example, system 100 can operate in accordance with any 5G, nextgeneration communication technology, or existing communicationtechnologies, various examples of which are listed supra. In thisregard, various features and functionalities of system 100 areapplicable where the devices (e.g., the UEs 102 and the network device104) of system 100 are configured to communicate wireless signals usingone or more multi carrier modulation schemes, wherein data symbols canbe transmitted simultaneously over multiple frequency subcarriers (e.g.,OFDM, CP-OFDM, DFT-spread OFMD, UFMC, FMBC, etc.). The embodiments areapplicable to single carrier as well as to multicarrier (MC) or carrieraggregation (CA) operation of the UE. The term carrier aggregation (CA)is also called (e.g. interchangeably called) “multi-carrier system”,“multi-cell operation”, “multi-carrier operation”, “multi-carrier”transmission and/or reception. Note that some embodiments are alsoapplicable for Multi RAB (radio bearers) on some carriers (that is dataplus speech is simultaneously scheduled).

In various embodiments, system 100 can be configured to provide andemploy 5G or subsequent generation wireless networking features andfunctionalities. 5G wireless communication networks are expected tofulfill the demand of exponentially increasing data traffic and to allowpeople and machines to enjoy gigabit data rates with virtually zero(e.g., single digit millisecond) latency. Compared to 4G, 5G supportsmore diverse traffic scenarios. For example, in addition to the varioustypes of data communication between conventional UEs (e.g., phones,smartphones, tablets, PCs, televisions, internet enabled televisions,AR/VR head mounted displays (HMDs), etc.) supported by 4G networks, 5Gnetworks can be employed to support data communication between smartcars in association with driverless car environments, as well as machinetype communications (MTCs). Considering the drastic differentcommunication needs of these different traffic scenarios, the ability todynamically configure waveform parameters based on traffic scenarioswhile retaining the benefits of multi carrier modulation schemes (e.g.,OFDM and related schemes) can provide a significant contribution to thehigh speed/capacity and low latency demands of 5G networks. Withwaveforms that split the bandwidth into several sub-bands, differenttypes of services can be accommodated in different sub-bands with themost suitable waveform and numerology, leading to an improved spectrumutilization for 5G networks.

To meet the demand for data centric applications, features of 5Gnetworks can comprise: increased peak bit rate (e.g., 20 Gbps), largerdata volume per unit area (e.g., high system spectral efficiency—forexample about 3.5 times that of spectral efficiency of long termevolution (LTE) systems), high capacity that allows more deviceconnectivity both concurrently and instantaneously, lower battery/powerconsumption (which reduces energy and consumption costs), betterconnectivity regardless of the geographic region in which a user islocated, a larger numbers of devices, lower infrastructural developmentcosts, and higher reliability of the communications. Thus, 5G networkscan allow for: data rates of several tens of megabits per second shouldbe supported for tens of thousands of users, 1 gigabit per second to beoffered simultaneously to tens of workers on the same office floor, forexample; several hundreds of thousands of simultaneous connections to besupported for massive sensor deployments; improved coverage, enhancedsignaling efficiency; reduced latency compared to LTE.

The 5G access network can utilize higher frequencies (e.g., >6 GHz) toaid in increasing capacity. Currently, much of the millimeter wave(mmWave) spectrum, the band of spectrum between 30 GHz and 300 GHz isunderutilized. The millimeter waves have shorter wavelengths that rangefrom 10 millimeters to 1 millimeter, and these mmWave signals experiencesevere path loss, penetration loss, and fading. However, the shorterwavelength at mmWave frequencies also allows more antennas to be packedin the same physical dimension, which allows for large-scale spatialmultiplexing and highly directional beamforming.

Performance can be improved if both the transmitter and the receiver areequipped with multiple antennas. Multi-antenna techniques cansignificantly increase the data rates and reliability of a wirelesscommunication system. The use of multiple input multiple output (MIMO)techniques, which was introduced in the 3GPP and has been in use(including with LTE), is a multi-antenna technique that can improve thespectral efficiency of transmissions, thereby significantly boosting theoverall data carrying capacity of wireless systems. The use of MIMOtechniques can improve mmWave communications and has been widelyrecognized as a potentially important component for access networksoperating in higher frequencies. MIMO can be used for achievingdiversity gain, spatial multiplexing gain and beamforming gain. Forthese reasons, MIMO systems are an important part of the 3rd and 4thgeneration wireless systems and are in use in 5G systems.

FIG. 2 illustrates example static and moving locations of userequipment, and example network measurements reported to network nodes ofa wireless communication system, in accordance with various aspects andembodiments of the subject disclosure. FIG. 2 includes example networknodes 211 and 212 and various example locations within a geographicalregion surrounding the network nodes 211 and 212.

The locations represent estimated locations visited by user equipment atdifferent times T1, T2, T3, T4, T5, T6, T7, T8 (the user equipmentitself is not illustrated in FIG. 2 for simplicity of illustration). Theestimated locations include “static” locations, at which the userequipment remained for a “long” period of time, e.g., for 15 minutes orlonger, and “moving” locations, visited by the user equipment as theuser equipment was in motion, e.g., along the illustrated route 240. Theestimated locations comprise T1 static location 201, T2 moving location202, T3 moving location 203, T4 moving location 204, T4 adjusted movinglocation 205, T5 moving location 206, T6 moving location 207, T7 movinglocation 208, T8 static location 209, and T8 adjusted static location210.

In FIG. 2 , the estimated T4 moving location 204 of the user equipmentat time T4 can be adjusted according to techniques disclosed herein,thereby identifying T4 adjusted moving location 205. Similarly, theestimated T8 static location 209 of the user equipment at time T8 can beadjusted according to techniques disclosed herein, thereby identifyingT8 adjusted static location 210. The T4 adjusted moving location 205 andthe T8 adjusted static location 210 can have higher accuracy than theinitially estimated locations 204, 209. The adjusted locations 205, 210can be stored along with other estimated location information 201, 202,203, 206, 207, and 208, to achieve improved location informationassociated with the user equipment. The resulting higher accuracylocation information can be used for any desired application, e.g., fornetwork planning or any other application.

In general, the techniques disclosed herein can include obtaining a timeseries of network measurement data associated with the user equipment.The time series of network measurement data can include networkmeasurement data collected at multiple different times, e.g., collectedat each of the illustrated times T1, T2, T3, T4, T5, T6, T7, T8. FIG. 2illustrates collection of example T1 network measurements 221 vianetwork node 211, collection of example T4 network measurements 222 vianetwork node 212, and collection of example T8 network measurements 223via network node 211. The network nodes 211 and 212, or any othernetwork nodes serving the user equipment, can similarly collect networkmeasurements at the other times T2, T3, T5, T6, and T7. Networkmeasurement data such as T1 network measurements 221, T4 networkmeasurements 222, and T8 network measurements 223 can be provided tonetwork equipment, e.g. network equipment included in the communicationservice provider network(s) 106 illustrated in FIG. 1 , and the networkequipment can process the network measurement data according to thetechniques disclosed herein. Example network equipment and operationsthereof are described further in connection with FIGS. 3-18 .

As will be described in further detail with reference to FIGS. 3-18 ,processing of network measurement data according to this disclosure cangenerally include sorting the network measurement data into dataassociated with static locations, such as T1 static location 201 and T8static location 209, and data associated with moving locations, such asmoving locations 202-208. Separate processing techniques can then beapplied to the static location data and the moving location data,followed by merge operations wherein static location information is usedto improve moving location information, and vice versa.

Furthermore, processing techniques applied to moving location dataassociated with moving locations 202-208 can include, inter alia,separating the moving location data into data associated with differentsegments 231 and 232. The different segments 231 and 232 can beassociated with different user equipment travel speeds. The movinglocation data associated with each segment 231 and 232 can be processedindependently, followed by merge operations wherein location informationassociated with the segments 231 and 232 can be joined with locationinformation from other segments as well as static location information.

FIG. 3 illustrates example network equipment configured to performmobility mode identification of static and moving user equipmentmobility modes, in accordance with various aspects and embodiments ofthe subject disclosure. FIG. 3 includes example network equipment 300,wherein the example network equipment 300 can be included incommunication service provider network(s) 106 illustrated in FIG. 1 ,and wherein the example network equipment 300 can furthermore obtainnetwork measurement data such as described with reference to FIG. 2 .

The network equipment 300 includes network measurement data store 350,general geotagging output 301, UE active session split 302, eliminateduplicate patterns 303, mobility mode identification 304, staticsmoothing 305, update high frequency static pattern 306, movingsmoothing 307, update high frequency route pattern 308, merge landmarks309, and UE routing output 310.

In general, with reference to FIG. 3 , the network equipment 300 can beconfigured to use UE active session split 302, eliminate duplicatepatterns 303, and mobility mode identification 304 to identify networkmeasurement data associated with static UE locations, and to identifynetwork measurement data associated with moving UE locations. Thenetwork equipment 300 can then use static smoothing 305 and update highfrequency static pattern 306 to process network measurement dataassociated with the static UE locations, and the network equipment 300can use moving smoothing 307 and update high frequency route pattern 308to process network measurement data associated with the moving UElocations. The network equipment 300 can then use merge landmarks 309 tofurther improve UE location information, and the network equipment 300can produce the UE routing output 310 comprising adjusted/improved UElocation information.

In an aspect, FIG. 3 provides a framework to improve UE routing with themeasurements reported by cells and eNodeBs in a telecommunicationsnetwork. Methods can utilize both the network measurement patterns andtime-sequences of UE locations estimated by general online geotaggingprocesses. Methods can first determine the mobility status of a UEacross various timestamps, i.e., whether the UE is static or moving.Methods can then split a UE route into multiple mobility periods, withineach of which the UE mobility status is substantially unchanged. Foreach mobility period, methods can apply suitable static/movingprocessing to further improve the location estimates. Methods can employthis divide and conquer approach in part because measurement noisecharacteristics tend to be quite different across static and movingmodes. Finally, methods can combine the estimates from the variousmobility periods together for a complete route. Methods can provide thecapability to identify UE mobility status and improve overall accuracyfor 5G and future wireless network geolocation.

In another aspect, FIG. 3 provides a framework for UE offline routingwhich utilizes the time sequence of wireless network measurements andestimated UE locations via general geotagging processes. Embodiments canlearn the time series of network measurement patterns and builddatabases to reinforce the learning to determine the mobility status ofUEs effectively. Mobility mode identification 304 can split UE routesinto moving periods and static periods. Network equipment 300 can thenapply different (static/moving) smoothing functions 305, 307 tocorresponding mobility periods. Embodiments can furthermore design thereference points to exchange between different mobility periods whichhelps form seamless routes when concatenating the location estimatesfrom such periods.

Network equipment 300 can enable improved UE routing in atelecommunication network. Embodiments can smooth individual UE routesby using a combination of primary location estimates based on generalgeolocation technologies, and secondary location estimates based onlearned time series measurement patterns to remove outliers. In someembodiments, a UE route can be represented using locations where thereis a change in the UE network measurement pattern. This reduces thecomputational effort by eliminating redundant location estimationcalculations. Embodiments can furthermore identify the UE mobilitystatus (whether static or moving), based on the UE network measurementpatterns across various time periods.

Network equipment 300 can utilize databases to store frequently observednetwork measurement patterns corresponding to static modes and routesfor specific UEs. Network equipment 300 can apply appropriate smoothingalgorithms adapted to the UE mobility status. For example, networkequipment 300 can reuse the patterns stored in the database when the UEis inferred to be static. Network equipment 300 can combine theestimates from the various time periods (where the UE could be inmultiple mobility modes) to recover the complete UE route.

FIG. 4 is a flow diagram representing example operations of networkequipment to perform mobility mode identification of static and movinguser equipment mobility modes, in accordance with various aspects andembodiments of the subject disclosure. The illustrated operations can beperformed, e.g., by network equipment 300 such as illustrated in FIG. 3.

At acquire data 402, network equipment 300 can acquire time-sequences ofhistorical geotagged call trace data. For example, network equipment 300can obtain general geotagging output 301 from network measurement datastore 350. The general geotagging output 301 can include, but need notbe limited to, historical geotagged call trace data comprising at leastone of international mobile subscriber identity (IMSI) information,timestamp information, timing advance information, signal strengthinformation, serving cell information, estimated latitude information,estimated longitude information, or geotagging type information.

The UE active session split 302 can be configured to identify differentperiods of UE activity within general geotagging output 301, andeliminate duplicate patterns 303 can be configured to eliminateduplicates in order to decrease the volume of data to be processed.

At mobility mode identification 404, network equipment 300 can beconfigured to apply UE mobility mode identification 304 to the UEnetwork measurements to mark each record as static or moving. UEmobility mode identification 304 can be configured to use this indicatorto split each UE's measurements time series into static periods andmoving periods.

At stabilize static location estimates 406, network equipment 300 can beconfigured to apply static smoothing 305 to stabilize the estimatedlocations in each static period. At stabilize moving location estimates408, network equipment 300 can be configured to apply moving smoothing307 to remove outliers and estimate a robust route for each of themoving periods. At apply static and moving labels 410, network equipment300 can be configured to use update high frequency static pattern 306and update high frequency route pattern 308, respectively, to applylabels such as “static pattern”/“moving pattern” labels to networkmeasurement/estimated location pairs. Network equipment 300 can beconfigured to feed this data back to UE mobility mode identification 304and static/moving smoothing 305, 307 to refine the patterns over time,enabling process speed up. At combine estimated locations 412, networkequipment 300 can be configured to combine the estimated locations fromthe various periods to generate a complete time-series of locationestimates for each UE.

FIG. 5 is a flow diagram representing example operations of networkequipment to prepare a time series of historical geotagged call tracedata, in accordance with various aspects and embodiments of the subjectdisclosure. The illustrated operations can be performed, e.g., bynetwork equipment 300 such as illustrated in FIG. 3 .

At obtain geotagging time series 502, network equipment 300 can beconfigured to obtain general geotagging output 301 comprising a timeseries of call trace records. The output 301 can contain the networkmeasurements and estimated location information such as U_(t)=(IMSI,timestamp, network measurements, EST_LAT, EST_LON, alg_info), wherealg_info indicates the geotagging technology used and the associatedaccuracy, EST_LAT is an estimated latitude, and EST_LON is an estimatedlongitude. Further network measurements can include, e.g., globalconnectivity index (GCI), timing advance (TA), reference signal receivedpower (RSRP), reference signal received power (RSRQ), or othermeasurement information.

At split time series into active sessions 504, for a given UE, networkequipment 300 can apply UE active session split 302 to split the timeseries into active sessions based on the record timestamps. This caninclude sorting the records in a given time series by timestamp;including records within a specified time interval, δ_(t) (for example,δ_(t)=10 minutes), of the previous timestamp, into a same activesession; and otherwise, generating a new active session starting with agiven record.

At remove redundant records 506, within a given active session, networkequipment 300 can be configured to run eliminate duplicate patterns 303to remove redundant UE records. This can include, e.g., ignoring thetimestamp, and comparing each record information with a previous record.If the record information is duplicated, then remove the current record.This method keeps new information in a time series, thus reducing thecomputational burden. In real-world networks, network measurements canrepeat many times over and the corresponding location estimates can beidentical as well. The number of records can be reduced significantly,without sacrificing the accuracy of later steps.

FIG. 6 is a flow diagram representing example operations of networkequipment to apply mobility mode identification, in accordance withvarious aspects and embodiments of the subject disclosure. Theillustrated operations can be performed, e.g., by network equipment 300such as illustrated in FIG. 3 .

At static pattern inference 602, for each record, mobility modeidentification 304 can compare the network measurements (such as servingcell and timing advance) with a UE's known static patterns. The knownstatic pattern can be based on a long time history (e.g., months) of UEcall trace records. The locations frequently visited by a UE (alsoreferred to herein as frequently visited places or FVPs) can beidentified and estimated with corresponding serving cell and TA values.Since the UE is static at those FVP locations, the associatedmeasurements (IMSI, serving cell, TA) patterns, if encountered in thefuture, can be used to infer that the UE is static. FVP patternscomprise an initial set of UE static patterns. If matched, mobility modeidentification 304 can mark the record with a ‘static’ tag. Otherwise,mobility mode identification 304 can proceed to the next step.

At long duration inference 604, mobility mode identification 304 canapply long duration inferences. For example, mobility modeidentification 304 can check the duration (the difference between atimestamp of a next change and a current timestamp) of a networkmeasurement. If the duration is larger than a predetermined value, e.g.,a “static cutoff” value such as 10 minutes, the record can be markedwith a ‘static’ tag. Otherwise mobility mode identification 304 canproceed to the next step.

At static interpolation 606, mobility mode identification 304 canperform static interpolation. If the duration is short, mobility modeidentification 304 can calculate a static gap as a difference between atimestamp of a next tagged static record and a timestamp of a previoustagged static record. If static gap is smaller than a predeterminedvalue, e.g., a “static_cutoff2” such as 5 minutes, the record can bemarked with a ‘static’ tag since the UE is static right before andafter.

At moving inference 608, mobility mode identification 304 can performmoving inference. Mobility mode identification 304 can mark recordswithout a ‘static’ tag as ‘moving’. At moving interpolation 610,mobility mode identification 304 can then perform moving interpolation,wherein mobility mode identification 304 can re-evaluate records taggedas ‘static’. For each such record, mobility mode identification 304 cancalculate the moving gap as the difference between a timestamp of a nexttagged moving record and a timestamp of a previous tagged static record.If moving gap is smaller than a predetermined value, e.g., a “movingcutoff” value such as 5 minutes, mobility mode identification 304 canrevise the tag of this record as ‘moving’ since the UE is moving rightbefore and after.

At static-mobility split 612, mobility mode identification 304 canperform a static-mobility split in which mobility mode identification304 splits an active session time series into static and moving periodsbased on the tag of each record, by adding consecutive records taggedsimilarly to the same period if the time gap is small, such as 10minutes or less.

FIG. 7 is a flow diagram representing example operations of networkequipment to estimate UE routes, in accordance with various aspects andembodiments of the subject disclosure. The illustrated operations can beperformed, e.g., by network equipment 300 such as illustrated in FIG. 3.

At refine static location estimates 702, for UE records in a staticperiod, network equipment 300 can apply static smoothing 305 to refinethe location estimates. The output can be represented, e.g., as (IMSI,timestamp, network measurements, EST_LAT static, EST_LON static). When aspecific (IMSI, network measurements) pattern is observed with highfrequency over a long history, the “update high frequency staticpattern” component 306 can mark the pattern as a high frequency staticpattern, and the pattern can be stored with location indicated as(EST_LAT static, EST_LON static). This location estimate can be appliedwhen the pattern is observed in the static periods of future timeseries.

At refine moving location estimates 704, for UE records in a movingperiod, network equipment 300 can apply moving smoothing 307 to improvethe locations estimated in the period, to form a practical route. Thecorresponding output can be represented as (IMSI, timestamp, networkmeasurements, EST_LAT moving, EST_LON moving). If such a moving pattern(IMSI, network measurements) is observed repeatedly over the historicaltrace of the UE, then update high frequency route pattern 308 can storethe pattern with locations (EST_LAT moving, EST_LON moving), in order toguide UE mobility mode identification 304 and moving smoothing 307 infuture session time series.

At share border location estimates 706, records at the border of a“mobility mode” change can be shared between static smoothing 305 andmoving smoothing 307. For example, for a moving period, the staticrecords right before and/or after this period can be added to the movingperiod time series as reference points. Moving smoothing 307 need notchange the location estimation of those static points but can use thoselocations to regulate the estimated points when UE is moving. Similarly,the moving locations right before and/or after a static period can alsobe included and referred by static smoothing 305.

At retrieve active session landmarks 708, merge landmarks 309 can beconfigured to combine estimated locations from static smoothing 305 andmoving smoothing 307 to retrieve active session landmarks. Landmarks canbe represented as (IMSI, timestamp, network measurements, EST_LAT*,EST_LON*), where EST_LAT*=EST_LAT static or EST_LAT moving andEST_LON*=EST_LON static or EST_LON moving. At interpolate non-landmarkrecords 710, for records in an original active session time series butnot in landmarks, merge landmarks 309 can interpolate the UE locationsbased on timestamps between landmarks.

FIG. 8 illustrates example network equipment configured to estimatestatic user equipment locations based on network measurement data, inaccordance with various aspects and embodiments of the subjectdisclosure. FIG. 8 includes example network equipment 800, wherein theexample network equipment 800 can optionally process network measurementdata associated with static user equipment locations. As such, aspectsof network equipment 800 can optionally be included in the networkequipment 300 described with reference to FIG. 3 , and vice versa. Forexample, in some embodiments, network equipment 800 can implement, interalia, the static smoothing 305 illustrated in FIG. 3 .

The network equipment 800 includes network measurement data store 350,geotagged time series 801, mobility mode identification 304, geotaggingaccuracy filter 802, location estimates candidate set 803, reliabilitycalculation 804, geotagged static output 805, static patterns data store806, and FVP static patterns 807.

In general, with reference to FIG. 8 , the network equipment 800 can beconfigured to identify static UEs and estimate their location accuratelyusing measurements reported by cells and eNodeB s in atelecommunications network. The network equipment 800 can utilizenetwork measurement patterns learned over time to identify static UEsand select the most reliable location estimates. The network equipment800 can therefore implement a framework to identify and geolocate staticUEs in a telecommunication network. The network equipment 800 canutilize historical UE network measurement data to learn measurementpatterns associated with static UEs, which can be stored in a staticpattern database such as FVP static patterns 807. Given new UE networkmeasurement data observed over shorter timespans, the network equipment800 can use the learned static pattern information to determine whetherthe UE is static or not, and to estimate UE location during the statictime periods. The network equipment 800 can estimate locations within agiven static time period together, based on a derived reliabilitymeasure.

FIG. 9 is a flow diagram representing example operations of networkequipment to estimate static user equipment locations based on networkmeasurement data, in accordance with various aspects and embodiments ofthe subject disclosure. The illustrated operations can be performed,e.g., by network equipment 800 such as illustrated in FIG. 8 .

At acquire data 902, the network equipment 800 can be configured toacquire a time series of historical geotagged call trace data such asgeotagged time series 801, which can be obtained for example fromnetwork measurement data store 350. The geotagged time series 801 caninclude, but need not be limited to, IMSI, timestamp, timing advance,signal strength, serving cell, estimated latitude, estimated longitude,and geotagging type information. The geotagging type information caninclude, e.g., identifications of geotagging methodologies used forgeotagging, and associated accuracy information.

At mobility mode identification 904, the network equipment 800 can nextapply mobility mode identification 304 to each record within geotaggedtime series 801, in order to determine whether a UE is static or inmotion. Based on the determinations, mobility mode identification 304can extract the periods where the UE is static.

At derive candidate set of estimated static locations 906, within eachstatic period, the network equipment 800 can derive a candidate set ofestimated locations based on geotagging accuracy. At calculatereliability of candidate locations 908, reliability of each candidatelocation can be calculated based on network measurements observed withinthat static time period. A subset of “most reliable” location estimatescan be determined, optionally according to pre-specified rules. Thesereliable location estimates can be assigned to the call trace records inthe static period.

At static landmark merge 910, network equipment 800 can next apply astatic landmark merge algorithm to identify incorrectly tagged records,such as static records tagged as moving records due to measurementsobserved with large noise. The network equipment 800 can mergeconsecutive static periods to improve the estimation accuracy.

At apply static pattern labels and refine 912, the network equipment 800can be configured to apply “static pattern” labels to networkmeasurement/estimated location pairs corresponding to the “mostreliable” location estimates. The network equipment 800 can feed thisdata back to the UE mobility mode identification 304 and, e.g. to thestatic smoothing 305 illustrated in FIG. 3 , to refine the patterns overtime, enabling the process to speed up over time.

The network equipment 800 can include mobility mode identification 304,introduced in FIG. 3 . Example operations of mobility modeidentification 304 are previously described with reference FIG. 6 .

FIG. 10 illustrates example recurring network measurement data that canbe used to identify static user equipment, in accordance with variousaspects and embodiments of the subject disclosure. FIG. 10 includes atime series of network measurement data associated with a UE. FIG. 10includes a timeline comprising different cell and TA values associatedwith the UE. The cell and TA values include, e.g., CELL2, TA2, followedby CELL1, TA1, followed by CELL2, TA2, followed by CELL1, TA1, followedby CELL5, TA3, followed by CELL1, TA1, followed by CELL1, TA1, followedby CELL4, TA4, followed by CELL5, TA5, followed by CELL1, TA1. Anexample CELL2, TA2 recurrent period 1002 comprises recurring instancesof CELL2, TA2. An example CELL1, TA1 recurrent period 1004 comprisesrecurring instances of CELL1, TA1.

FIG. 11 is a flow diagram representing example operations of networkequipment to perform a static recurrent pattern identification process,in accordance with various aspects and embodiments of the subjectdisclosure. FIG. 11 can be understood by reference to FIG. 10 , and canbe performed for example by network equipment 800 illustrated in FIG. 8. At check recurrent time interval 1102, for a UE observed (cell, TA),network equipment 800 can check a recurrent time interval, defined asthe time difference between a next time minus a current time ofobserving a same (cell, TA). If the recurrent time interval is less thanor equal to a predetermined value, e.g. a recurrent_session_cutoff valuesuch as 10 minutes, then network equipment 800 can consider the UE to bein a same recurrent period and can assign a recurrent period identifierrecurrent_period_id.

At group recurrent records 1104, network equipment 800 can be configuredto group by (recurrent_period_id, cell, TA), and compute a max_time ofmax(timestamp), a min_time of min(timestamp), and a corresponding periodduration, defined as recurrent_duration=max_time-min_time. Ifrecurrent_duration is greater than or equal to a predetermined recurrenttime cutoff value such as 4 minutes, network equipment 800 can mark thewhole recurrent period (recurrent_period_id, min_time, max_time) as a‘static’ recurrent period.

At apply static labels 1106, network equipment 800 can be configured tocheck if, for any UE records, the timestamp is within any staticrecurrent period (recurrent_period_id, min_time, max_time). If yes,network equipment 800 can mark the timestamp as ‘static’.

FIG. 12 is a flow diagram representing example operations of networkequipment to perform a static geolocation process, in accordance withvarious aspects and embodiments of the subject disclosure. Theillustrated operations can be performed, e.g., by network equipment 800such as illustrated in FIG. 8 .

At identify candidate location set 1202, network equipment 800 candetermine a candidate location set. For each geolocation estimate in thegeotagged time series of call trace records from a given static period,geotagging accuracy filter 802 can identify the geotagging method.Example geotagging methods may include, but are not limited to,fingerprinting, FVP geotagging, and handover arc intersectioncalculation.

If the accuracy of a geotagging method identified by geotagging accuracyfilter 802 is within an acceptable range (e.g. median of 100 meters orless, 75% 200 meters or less), then geotagging accuracy filter 802 canadd the estimated location to the location estimates candidate set 803.If no geolocation method has accuracy within the acceptable range, thengeotagging accuracy filter 802 can pick a location with a best relativeaccuracy and add it to the location estimates candidate set 803.Optionally, geotagging accuracy filter 802 can confirm that estimatedstatic locations are within a reasonable range of moving UE locationsoccurring immediately before and after a current static time period,considering the speed of motion required to traverse the distancebetween those locations.

At location estimation 1204, network equipment 800 can perform locationestimation. If the candidate set 803 has a single element, then networkequipment 800 can use it as the location estimate for up to every recordwithin a static period. If there are multiple points in the candidateset 803, then network equipment 800 can use a suitable interpolationtechnique (e.g., linear, spline regression, median calculation) toderive a location estimate for the records in a static period.

At reliability calculation and pattern generation 1206, reliabilitycalculation 804 can process the candidate set 803. For each location inthe candidate set 803 and each record in the static period time series,reliability calculation 804 can compute: (1) a distance differencedefined as IUE to cell distance/78— TA|; and (2) an azimuth gap definedas |Cell to UE azimuth—Cell azimuth|. Furthermore, for each location inthe candidate set 803, reliability calculation 804 can count the numberof records where the distance difference is smaller than a distancecutoff (e.g. 2) and the azimuth gap is smaller than an azimuth cutoff(e.g. 90).

If the resulting count is high (e.g. three or more), then reliabilitycalculation 804 can mark a location along with the corresponding networkmeasurement (IMSI, serving cell, TA, est_lat, est_lon) as highlyreliable. Furthermore, if a reliable location is newly observed,reliability calculation 804 can add it to the static patterns data store806. The static pattern data store 806 can include patterns learnedusing multiple different methods, and can also include FVP staticpattern 807. The static pattern data store 806 can enable mobility modeidentification 304 as well as UE geolocation estimation when a relevantpattern is observed within a static time period.

At location estimation within static time period 1208, network equipment800 can perform location estimation within a static time period, inorder to generate geotagged static output 805. Network equipment 800 canpick a location estimate ranked first with highest count, optionallybreaking ties using the average of distance difference and azimuth gap.Network equipment 800 can apply the selected location estimate for:either all records within the static period, or only those records wherethe corresponding location estimates are not included in the reliablelocation estimate set.

At static landmark merge 1210, a static landmark merge process can mergesome static periods and re-run operations, starting with geotaggingaccuracy filter 802, for changed static periods. In an example staticlandmark merge process, first, if any two consecutive static periodswith estimated locations within a predetermined distance_cutoff value,such as 50 meters, and the time gap between the two periods is within apredetermined merge_cutoff value, such as 10 minutes, then the movingperiod between these two static sessions can be changed to be static.Second, the static landmark merge process can merge the three periodsinto a single static period. The first and second operations can beapplied to all periods and merges can be performed if necessary.Afterwards, the static location process can optionally be re-run.

FIG. 13 illustrates example network equipment configured to estimatemoving user equipment locations based on network measurement data, inaccordance with various aspects and embodiments of the subjectdisclosure. FIG. 13 includes example network equipment 1300, wherein theexample network equipment 1300 can optionally process networkmeasurement data associated with moving user equipment locations. Assuch, aspects of network equipment 1300 can optionally be included inthe network equipment 300 described with reference to FIG. 3 , and viceversa. For example, in some embodiments, network equipment 1300 canimplement the moving smoothing 307 illustrated in FIG. 3 .

The network equipment 1300 includes network measurement data store 350,geotagged time series 1301, mobility mode identification 304, geotaggingcomparison 1302, location estimates weight assignment 1303, weightedsmoothing 1304, snap routes on road 1305, geotagged moving UE output1306, moving patterns data store 1308, and geographic clutterinformation 1307.

In general, with reference to FIG. 13 , the network equipment 1300 canbe configured to implement a framework for moving UE identification androute estimation with the measurements reported by cells and eNodeB s inthe telecommunications network. Methods can utilize both the networkmeasurement patterns and time-sequences of UE locations estimated bygeneral online geotagging technologies. In a first step, mobility modeidentification 304 can be used to determine a time period where the UEis in motion. Subsequently, a moving location refinement process can beapplied to the geolocation estimates from such moving time periods. Themoving location refinement process can remove outliers and smooth theoverall route. This process can consider the geotagging method/accuracyof individual location estimates as well as further information,including geographic clutter (for example, road type) and speed oftravel.

In one proposed approach, embodiments according to FIG. 13 can include aframework to identify moving UEs and estimate their routes in atelecommunication network. Embodiments can extract time periods wherethe UE is in motion, from the time series of network measurements, via aUE “mobility mode” identification process. Embodiments can furthermoreapply a smoothing processing to remove outliers and estimate a robustroute based on geographic clutter (road types) information. A “movingpattern” label can be applied to network measurement/estimated locationpairs. This data can then be fed back to the UE mobility modeidentification process and smoothing processing to refine the patternsover time, enabling process speed up over time.

FIG. 14 is a flow diagram representing example operations of networkequipment to estimate moving user equipment locations based on networkmeasurement data, in accordance with various aspects and embodiments ofthe subject disclosure. The illustrated operations can be performed,e.g., by network equipment 1300 such as illustrated in FIG. 13 .

At acquire data 1402, network equipment 1300 can acquire time-sequencesof historical geotagged call trace data, such as geotagged time series1301. Geotagged time series 1301 can include, but is not limited to,IMSI, timestamp, timing advance, signal strength, serving cell,estimated latitude, estimated longitude, and geotagging typeinformation. The geotagging type information can include, e.g.,identifications of geotagging methodologies used for geotagging, andassociated accuracy information.

At mobility mode identification 1404, network equipment 1300 can beconfigured to apply mobility mode identification 304 to the networkmeasurements acquired at 1402, to mark each record UE's status as staticor moving. Based on the status, mobility mode identification 304 canform UE moving periods.

At extract moving time periods 1406, network equipment 1300 can beconfigured to extract the time periods where the UE is in motion. Foreach record within such periods, location estimates weight assignment1303 can calculate weights based on the estimation accuracy of thegeotagging method and the clutter type associated with the estimatedlocation and the implied UE speed.

At smoothing processing 1408, weighted smoothing 1304 can apply a robustsmoothing process wherein the weights calculated operation 1406 can beapplied to individual records. The resulting route can be snapped toroads, if applicable.

At apply moving pattern labels and refine 1410, network equipment 1300can apply a “moving pattern” label to each network measurement/estimatedlocation pair. Network equipment 1300 can feed this data back to the UEmobility mode identification 304 and moving UE route smoothingprocessing 1304 to refine the patterns over time, enabling process speedup.

The network equipment 1300 can include mobility mode identification 304,introduced in FIG. 3 . Example operations of mobility modeidentification 304 are previously described with reference FIG. 6 .

FIG. 15 is a flow diagram representing example operations of networkequipment to perform moving smoothing adjustments of estimatedlocations, in accordance with various aspects and embodiments of thesubject disclosure. The illustrated operations can be performed, e.g.,by network equipment 1300 such as illustrated in FIG. 13 .

At weight assignment 1502, geotagging comparison 1302 and locationestimates weight assignment 1303 can assign weights to locationestimates. Operations associated with weight assignment 1502 can includeassessing geotagging method accuracy, clutter matching, and optionally,confirmation of starting and ending locations.

In order to assess geotagging method accuracy, for each geolocationestimate in the time series of geotagged call trace records from a givenmoving period, geotagging comparison 1302 can identify the geotaggingtechnique. Examples of geotagging techniques include, but are notlimited to, fingerprinting, FVP geotagging, and handover arcintersection calculation. Geotagging comparison 1302 can form a set ofcandidate locations, comprising estimates located by geotaggingtechniques with accuracy within an acceptable range, e.g. with medianless than or equal to 500 meters. Location estimates weight assignment1303 can assign weights to each location estimate based on thegeotagging technique accuracy.

In order to perform clutter matching, location estimates weightassignment 1303 can estimate the UE speed based on prior locationestimates. For example, the speed can be estimated as the distancebetween consecutive geolocation estimates divided by the intervalbetween the corresponding timestamps. Location estimates weightassignment 1303 can then compare the estimated speed with a clutter typeassociated with the location estimate. The clutter type can be retrievedfrom geographic clutter information 1307. Location estimates weightassignment 1303 can assign higher weights to those location estimateswhere the clutter type is in alignment with the speed level—for example,“primary road” clutter type and speed estimate in excess of 50 mph. Ifthere is a match discrepancy, then location estimates weight assignment1303 can assign lower weights to those location estimates.

In order to confirm starting and ending locations, location estimatesweight assignment 1303 can confirm that the estimated starting andending locations are within a reasonable range of static UE locationsimmediately before and after a current moving time period. Embodimentscan utilize, for example the speed of motion required to traverse thedistance between those locations.

At smoothing processing 1504, weighted smoothing 1304 can divide a timeperiod where a UE is in motion into multiple segments so that eachsegment corresponds to a limited set of serving cells (e.g. no more than10) and/or a similar speed level of the UE based on the time intervalbetween points.

Within each segment, weighted smoothing 1304 can perform weightedsmoothing processing (for example, spline regression) to smooth thelocation estimates. Optionally, weighted smoothing 1304 can includestatic locations before and after a moving period as reference points.

Furthermore, within each segment, weighted smoothing 1304 can apply arobust method such as bootstrapping to remove the impact of outliers onthe smoothing. For example, weighted smoothing 1304 can randomly selecta subset of location estimates and repeat, starting from division intosegments. Weighted smoothing 1304 can identify outliers, e.g., originalestimated locations that are far away from smoothened locations.Weighted smoothing 1304 can remove these outliers and redo weightedsmoothing processing on the remaining points. Weighted smoothing 1304can apply the smoothing model to the timestamp of outliers to estimateUE locations.

Furthermore, within each segment, weighted smoothing 1304 can includethe location estimates right before and after the segment to form areasonable continuous route.

After processing the segments, weighted smoothing 1304 can concatenatethe smoothed location estimates from multiple segments, for example,weighted smoothing 1304 can concatenate the smoothed location estimatesfrom up to all segments. After the concatenation, weighted smoothing1304 can evaluate smoothened estimated location distances from originalestimated locations. If the distance is small, e.g., smaller than apredetermined distance, weighted smoothing 1304 can mark the smoothenedlocation as a reliable estimated point. If the distance is large, e.g.,larger than the predetermined distance, weighted smoothing 1304 can useinterpolation of nearby reliable smoothened points as the final smoothedestimation.

At pattern generation 1506, network equipment 1300 can store patterns inmoving patterns data store 1308. Each record denoted by (IMSI, servingcell, TA, est_lat, est_lon) can form a pattern. Network equipment 1300can maintain a tally of the number of times each pattern is observed. Ifthe number of observations of a given pattern exceeds a certainthreshold (N), then (if it doesn't already exist in the data store)network equipment 1300 can add it to the moving patterns data store1308.

At snap to road 1508, snap routes on road 1305 can optionally snapsmoothed location estimates to road topology. Based on the smoothedestimated locations, snap routes on road 1305 can recalculate the UEspeed. Using the geographical clutter information 1307, snap routes onroad 1305 can identify a closest road segment corresponding to eachestimated location, which matches the UE speed level. Snap routes onroad 1305 can then adjust locations to snap the estimated locations ontothe roads. If any sub-segment of the resulting route is deemedimplausible, then network equipment 1300 can rerun the weightedsmoothing 1304 on that sub-segment. Snap routes on road 1305 can repeatits operations until the route segment aligns with the geographicalclutter data. Results of the operations according to FIG. 15 cancomprise geotagged moving UE output 1306.

FIG. 16 is a flow diagram representing example operations of networkequipment to assign a static location as an estimated user equipmentlocation during a static period, in accordance with various aspects andembodiments of the subject disclosure. The illustrated blocks canrepresent actions performed in a method, functional components of acomputing device, or instructions implemented in a machine-readablestorage medium executable by a processor. While the operations areillustrated in an example sequence, the operations can be eliminated,combined, or re-ordered in some embodiments.

The operations illustrated in FIG. 16 can be performed, for example, bynetwork equipment 800 such as illustrated in FIG. 8 . Example operation1602 comprises obtaining, by network equipment 800 comprising aprocessor, a time sequence of network measurement data, e.g., geotaggedtime series 802, associated with a user equipment connected via anetwork comprising the network equipment. The user equipment cancomprise a user equipment 102 which visits, for example, the locationsillustrated in FIG. 2 . The time sequence of network measurement datacan comprise historical geotagged call trace data comprising at leastone of international mobile subscriber identity information, timestampinformation, timing advance information, signal strength information,serving cell information, estimated latitude information, estimatedlongitude information, or geotagging type information.

Example operation 1604 comprises identifying, by the network equipment800, within the time sequence of network measurement data 801, a staticperiod in which the user equipment remained static. Any of a number ofapproaches disclosed herein can be used to identify the static period.For example, identifying the static period can comprise identifying arecurring pattern of network measurement data within the time sequenceof network measurement data. The recurring pattern of networkmeasurement data can comprise network measurement data that repeatswithin a predetermined time interval. For example, repeating cell and TAinformation within an interval such as 10 or 15 minutes can indicate astatic UE.

Example operation 1606 comprises deriving, by the network equipment 800,based on network measurement data during the static period, a candidategroup of estimated locations. Deriving the candidate group of estimatedlocations can comprise, e.g., selecting network measurement datacomprising estimated location information, based on geotagging typeinformation associated with the network measurement data during thestatic period. Estimated location information which was generated bymore accurate geotagging types can be selected for inclusion in thecandidate group of estimated locations.

Example operation 1608 comprises assigning, by the network equipment800, based on the network measurement data during the static period,reliability scores to estimated locations of the candidate group ofestimated locations. In some embodiments, assigning the reliabilityscores to estimated locations can comprise comparing first distanceinformation derived from the network measurement data during the staticperiod with second distance information derived from the networkmeasurement data during the static period. For example, the firstdistance information can comprise a UE to cell distance based ongeotagging location information included in the network measurementdata, and the second distance information can comprise a UE to celldistance timing advance information included in the network measurementdata. More accurate/reliable geotagging location information willgenerally have a smaller difference from the distance calculated usingtiming advance information, and so smaller differences can be associatedwith higher reliability scores, and vice versa.

In some embodiments, assigning the reliability scores to estimatedlocations of the candidate group of estimated locations can comprisecomparing first azimuth information derived from the network measurementdata during the static period with second azimuth information derivedfrom the network measurement data during the static period. Again,azimuth based on geotagging location information can be compared withreported azimuth information, and higher reliability scores can beapplied when the differences are smaller, and vice versa.

Example operation 1610 comprises selecting, by the network equipment800, based on the reliability scores, an estimated location of thecandidate group of estimated locations, and example operation 1612comprises assigning, by the network equipment 800, the estimatedlocation to represent a location of the user equipment during the staticperiod.

Example operation 1614 comprises merging, by the network equipment 800,a consecutive static period with the static period, wherein theestimated location represents the location of the user equipment duringthe static period and the consecutive static period. For example, whenlocation information is similar across two consecutive static periods,the most reliable estimated location from one of the static periods, asdetermined by operations 1602-1612, can be applied to both consecutiveperiods.

Example operation 1616 comprises reclassifying, by the network equipment800, a moving period between the static period and the consecutivestatic period, resulting in a reclassified static period, and merging,by the network equipment 800, the reclassified static period with thestatic period and the consecutive static period. For example, a shortduration moving period between two consecutive static periods may be dueto incorrect/noisy network data. Therefore, the moving period can bereclassified as a static period, the location applied to the surroundingstatic periods can be assigned, and all three periods can be merged as asingle static period.

FIG. 17 is a flow diagram representing example operations of networkequipment to reclassify a user equipment moving period as a static,non-moving period, and assigning a static location to the reclassifiedperiod, in accordance with various aspects and embodiments of thesubject disclosure. The illustrated blocks can represent actionsperformed in a method, functional components of a computing device, orinstructions implemented in a machine-readable storage medium executableby a processor. While the operations are illustrated in an examplesequence, the operations can be eliminated, combined, or re-ordered insome embodiments.

The operations illustrated in FIG. 17 can be performed, for example, bynetwork equipment 800 such as illustrated in FIG. 8 . Example operation1702 comprises obtaining a time sequence of network measurement dataassociated with a user equipment, such as geotagged time series 801.Example operation 1704 comprises identifying, within the time sequenceof network measurement data 801, a static period in which the userequipment remained static. For example, mobility mode identification 304can identify static periods as described herein. In some embodiments,identifying the static period can comprise identifying a recurringpattern of network measurement data within the time sequence of networkmeasurement data, for example, as described in connection with FIG. 10and FIG. 11 . The recurring pattern of network measurement data cancomprise network measurement data that repeats within a predeterminedtime interval.

Example operation 1706 comprises merging a consecutive static periodwith the static period. The consecutive static period can optionally beseparated from the static period by other periods of different types,e.g., by moving periods. Example operation 1708 comprises reclassifyinga moving period between the static period and the consecutive staticperiod, resulting in a reclassified static period. The reclassifiedstatic period is the period that was formerly classified as a movingperiod.

Example operation 1710 comprises merging the reclassified static periodwith the static period and the consecutive static period, resulting in amerged static period. A best location estimate can then be applied tothe merged static period, pursuant to operations 1712-1716.

Example operation 1712 comprises deriving, based on network measurementdata during the merged static period, a candidate group of estimatedlocations, and selecting the estimated location from the candidate groupof estimated locations. Operation 1714 can enable the selection. Exampleoperation 1714 comprises assigning, based on the network measurementdata during the merged static period, reliability scores to estimatedlocations of the candidate group of estimated locations. Selecting theestimated location can be based on the reliability scores. For example,assigning the reliability scores can comprise comparing first distanceand/or azimuth information derived from the network measurement dataduring the static period with second distance and/or azimuth informationderived from the network measurement data during the static period, asdescribed above with reference to FIG. 16 .

Example operation 1716 comprises assigning an estimated location torepresent a location of the user equipment during the merged staticperiod. For example, the estimated location selected from among thecandidate group of estimated locations can be assigned to represent alocation of the user equipment during the merged static period.

FIG. 18 is a flow diagram representing example operations of networkequipment to assign a static location as an estimated user equipmentlocation during a static period based on reliability scores of estimatedlocation information pertaining to the static period, in accordance withvarious aspects and embodiments of the subject disclosure. Theillustrated blocks can represent actions performed in a method,functional components of a computing device, or instructions implementedin a machine-readable storage medium executable by a processor. Whilethe operations are illustrated in an example sequence, the operationscan be eliminated, combined, or re-ordered in some embodiments.

The operations illustrated in FIG. 18 can be performed, for example, bynetwork equipment 800 such as illustrated in FIG. 8 . Example operation1802 comprises deriving, based on network measurement data from astationary, i.e., static period of a mobile device, candidate estimatedlocations of the mobile device. Deriving the candidate estimatedlocations can comprise, for example, selecting location information fromlocation information included in the network measurement data.

Example operation 1804 comprises assigning, based on the networkmeasurement data, reliability scores to estimated locations of thecandidate estimated locations. Assigning the reliability scores to theestimated locations can comprise, e.g. comparing first distanceinformation derived from the network measurement data during thestationary period, e.g., distance from a UE to a cell based on reportedgeotagging information, with second distance information derived fromthe network measurement data during the stationary period, e.g.,distance from a UE to a cell TA information. A smaller differencebetween the first distance information and the second distanceinformation corresponds to a higher reliability score, as describedherein.

In another aspect, assigning the reliability scores to the estimatedlocations can comprise comparing first azimuth information derived fromthe network measurement data during the stationary period, e.g., azimuthbased on reported geotagging information, with second azimuthinformation derived from the network measurement data during thestationary period e.g., azimuth based on reported serving cellinformation. A smaller difference between the first azimuth informationand the second azimuth information corresponds to a higher reliabilityscore, as described herein.

Example operation 1806 comprises selecting, based on the reliabilityscores, an estimated location of the candidate estimated locations.Example operation 1808 comprises assigning the estimated location torepresent a location of the mobile device during the stationary period.

FIG. 19 is a block diagram of an example computer that can be operableto execute processes and methods in accordance with various aspects andembodiments of the subject disclosure. The example computer can beadapted to implement, for example, any of the various network equipmentdescribed herein.

FIG. 19 and the following discussion are intended to provide a brief,general description of a suitable computing environment 1900 in whichthe various embodiments of the embodiment described herein can beimplemented. While the embodiments have been described above in thegeneral context of computer-executable instructions that can run on oneor more computers, those skilled in the art will recognize that theembodiments can be also implemented in combination with other programmodules and/or as a combination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, IoT devices, distributedcomputing systems, as well as personal computers, hand-held computingdevices, microprocessor-based or programmable consumer electronics, andthe like, each of which can be operatively coupled to one or moreassociated devices.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media, machine-readable storage media,and/or communications media, which two terms are used herein differentlyfrom one another as follows. Computer-readable storage media ormachine-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media or machine-readablestorage media can be implemented in connection with any method ortechnology for storage of information such as computer-readable ormachine-readable instructions, program modules, structured data orunstructured data.

Computer-readable storage media can include, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), smart card, flashmemory (e.g., card, stick, key drive) or other memory technology,compact disk (CD), compact disk read only memory (CD-ROM), digitalversatile disk (DVD), Blu-ray™ disc (BD) or other optical disk storage,floppy disk storage, hard disk storage, magnetic cassettes, magneticstrip(s), magnetic tape, magnetic disk storage or other magnetic storagedevices, solid state drives or other solid state storage devices, avirtual device that emulates a storage device (e.g., any storage devicelisted herein), or other tangible and/or non-transitory media which canbe used to store desired information. In this regard, the terms“tangible” or “non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and includes any information deliveryor transport media. The term “modulated data signal” or signals refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in one or more signals. By wayof example, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference again to FIG. 19 , the example environment 1900 forimplementing various embodiments of the aspects described hereinincludes a computer 1902, the computer 1902 including a processing unit1904, a system memory 1906 and a system bus 1908. The system bus 1908couples system components including, but not limited to, the systemmemory 1906 to the processing unit 1904. The processing unit 1904 can beany of various commercially available processors. Dual microprocessorsand other multi-processor architectures can also be employed as theprocessing unit 1904.

The system bus 1908 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1906includes ROM 1910 and RAM 1912. A basic input/output system (BIOS) canbe stored in a non-volatile memory such as ROM, erasable programmableread only memory (EPROM), EEPROM, which BIOS contains the basic routinesthat help to transfer information between elements within the computer1902, such as during startup. The RAM 1912 can also include a high-speedRAM such as static RAM for caching data.

The computer 1902 further includes an internal hard disk drive (HDD)1914 (e.g., EIDE, SATA), one or more external storage devices 1916(e.g., a magnetic floppy disk drive (FDD) 1916, a memory stick or flashdrive reader, a memory card reader, etc.) and an optical disk drive 1920(e.g., which can read or write from a CD-ROM disc, a DVD, a BD, etc.).While the internal HDD 1914 is illustrated as located within thecomputer 1902, the internal HDD 1914 can also be configured for externaluse in a suitable chassis (not shown). Additionally, while not shown inenvironment 1900, a solid state drive (SSD) could be used in additionto, or in place of, an HDD 1914. The HDD 1914, external storagedevice(s) 1916 and optical disk drive 1920 can be connected to thesystem bus 1908 by an HDD interface 1924, an external storage interface1926 and an optical drive interface 1928, respectively. The interface1924 for external drive implementations can include at least one or bothof Universal Serial Bus (USB) and Institute of Electrical andElectronics Engineers (IEEE) 1394 interface technologies. Other externaldrive connection technologies are within contemplation of theembodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1902, the drives andstorage media accommodate the storage of any data in a suitable digitalformat. Although the description of computer-readable storage mediaabove refers to respective types of storage devices, it should beappreciated by those skilled in the art that other types of storagemedia which are readable by a computer, whether presently existing ordeveloped in the future, could also be used in the example operatingenvironment, and further, that any such storage media can containcomputer-executable instructions for performing the methods describedherein.

A number of program modules can be stored in the drives and RAM 1912,including an operating system 1930, one or more application programs1932, other program modules 1934 and program data 1936. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1912. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

Computer 1902 can optionally comprise emulation technologies. Forexample, a hypervisor (not shown) or other intermediary can emulate ahardware environment for operating system 1930, and the emulatedhardware can optionally be different from the hardware illustrated inFIG. 19 . In such an embodiment, operating system 1930 can comprise onevirtual machine (VM) of multiple VMs hosted at computer 1902.Furthermore, operating system 1930 can provide runtime environments,such as the Java runtime environment or the .NET framework, forapplications 1932. Runtime environments are consistent executionenvironments that allow applications 1932 to run on any operating systemthat includes the runtime environment. Similarly, operating system 1930can support containers, and applications 1932 can be in the form ofcontainers, which are lightweight, standalone, executable packages ofsoftware that include, e.g., code, runtime, system tools, systemlibraries and settings for an application.

Further, computer 1902 can be enabled with a security module, such as atrusted processing module (TPM). For instance with a TPM, bootcomponents hash next in time boot components, and wait for a match ofresults to secured values, before loading a next boot component. Thisprocess can take place at any layer in the code execution stack ofcomputer 1902, e.g., applied at the application execution level or atthe operating system (OS) kernel level, thereby enabling security at anylevel of code execution.

A user can enter commands and information into the computer 1902 throughone or more wired/wireless input devices, e.g., a keyboard 1938, a touchscreen 1940, and a pointing device, such as a mouse 1942. Other inputdevices (not shown) can include a microphone, an infrared (IR) remotecontrol, a radio frequency (RF) remote control, or other remote control,a joystick, a virtual reality controller and/or virtual reality headset,a game pad, a stylus pen, an image input device, e.g., camera(s), agesture sensor input device, a vision movement sensor input device, anemotion or facial detection device, a biometric input device, e.g.,fingerprint or iris scanner, or the like. These and other input devicesare often connected to the processing unit 1904 through an input deviceinterface 1944 that can be coupled to the system bus 1908, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, a BLUETOOTH®interface, etc.

A monitor 1946 or other type of display device can be also connected tothe system bus 1908 via an interface, such as a video adapter 1948. Inaddition to the monitor 1946, a computer typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1902 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1950. The remotecomputer(s) 1950 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer1902, although, for purposes of brevity, only a memory/storage device1952 is illustrated. The logical connections depicted includewired/wireless connectivity to a local area network (LAN) 1954 and/orlarger networks, e.g., a wide area network (WAN) 1956. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theinternet.

When used in a LAN networking environment, the computer 1902 can beconnected to the local network 1954 through a wired and/or wirelesscommunication network interface or adapter 1958. The adapter 1958 canfacilitate wired or wireless communication to the LAN 1954, which canalso include a wireless access point (AP) disposed thereon forcommunicating with the adapter 1958 in a wireless mode.

When used in a WAN networking environment, the computer 1902 can includea modem 1960 or can be connected to a communications server on the WAN1956 via other means for establishing communications over the WAN 1956,such as by way of the internet. The modem 1960, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 1908 via the input device interface 1944. In a networkedenvironment, program modules depicted relative to the computer 1902 orportions thereof, can be stored in the remote memory/storage device1952. It will be appreciated that the network connections shown areexample and other means of establishing a communications link betweenthe computers can be used.

When used in either a LAN or WAN networking environment, the computer1902 can access cloud storage systems or other network-based storagesystems in addition to, or in place of, external storage devices 1916 asdescribed above. Generally, a connection between the computer 1902 and acloud storage system can be established over a LAN 1954 or WAN 1956e.g., by the adapter 1958 or modem 1960, respectively. Upon connectingthe computer 1902 to an associated cloud storage system, the externalstorage interface 1926 can, with the aid of the adapter 1958 and/ormodem 1960, manage storage provided by the cloud storage system as itwould other types of external storage. For instance, the externalstorage interface 1926 can be configured to provide access to cloudstorage sources as if those sources were physically connected to thecomputer 1902.

The computer 1902 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, store shelf, etc.), and telephone. This can include WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

The above description includes non-limiting examples of the variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the disclosed subject matter, and one skilled in the art canrecognize that further combinations and permutations of the variousembodiments are possible. The disclosed subject matter is intended toembrace all such alterations, modifications, and variations that fallwithin the spirit and scope of the appended claims.

With regard to the various functions performed by the above describedcomponents, devices, circuits, systems, etc., the terms (including areference to a “means”) used to describe such components are intended toalso include, unless otherwise indicated, any structure(s) whichperforms the specified function of the described component (e.g., afunctional equivalent), even if not structurally equivalent to thedisclosed structure. In addition, while a particular feature of thedisclosed subject matter may have been disclosed with respect to onlyone of several implementations, such feature may be combined with one ormore other features of the other implementations as may be desired andadvantageous for any given or particular application.

The terms “exemplary” and/or “demonstrative” as used herein are intendedto mean serving as an example, instance, or illustration. For theavoidance of doubt, the subject matter disclosed herein is not limitedby such examples. In addition, any aspect or design described herein as“exemplary” and/or “demonstrative” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent structures and techniques known to one skilled inthe art. Furthermore, to the extent that the terms “includes,” “has,”“contains,” and other similar words are used in either the detaileddescription or the claims, such terms are intended to be inclusive—in amanner similar to the term “comprising” as an open transitionword—without precluding any additional or other elements.

The term “or” as used herein is intended to mean an inclusive “or”rather than an exclusive “or.” For example, the phrase “A or B” isintended to include instances of A, B, and both A and B. Additionally,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unless eitherotherwise specified or clear from the context to be directed to asingular form.

The term “set” as employed herein excludes the empty set, i.e., the setwith no elements therein. Thus, a “set” in the subject disclosureincludes one or more elements or entities. Likewise, the term “group” asutilized herein refers to a collection of one or more entities.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

The description of illustrated embodiments of the subject disclosure asprovided herein, including what is described in the Abstract, is notintended to be exhaustive or to limit the disclosed embodiments to theprecise forms disclosed. While specific embodiments and examples aredescribed herein for illustrative purposes, various modifications arepossible that are considered within the scope of such embodiments andexamples, as one skilled in the art can recognize. In this regard, whilethe subject matter has been described herein in connection with variousembodiments and corresponding drawings, where applicable, it is to beunderstood that other similar embodiments can be used or modificationsand additions can be made to the described embodiments for performingthe same, similar, alternative, or substitute function of the disclosedsubject matter without deviating therefrom. Therefore, the disclosedsubject matter should not be limited to any single embodiment describedherein, but rather should be construed in breadth and scope inaccordance with the appended claims below.

What is claimed is:
 1. A method, comprising: obtaining, by networkequipment comprising a processor, a time sequence of network measurementdata associated with a user equipment connected via a network comprisingthe network equipment; identifying, by the network equipment, within thetime sequence of network measurement data, a static period in which theuser equipment remained static; deriving, by the network equipment,based on network measurement data during the static period, a candidategroup of estimated locations; assigning, by the network equipment, basedon the network measurement data during the static period, reliabilityscores to estimated locations of the candidate group of estimatedlocations; and selecting, by the network equipment, based on thereliability scores, an estimated location of the candidate group ofestimated locations; and assigning, by the network equipment, theestimated location to represent a location of the user equipment duringthe static period.
 2. The method of claim 1, wherein the time sequenceof network measurement data comprises historical geotagged call tracedata comprising at least one of international mobile subscriber identityinformation, timestamp information, timing advance information, signalstrength information, serving cell information, estimated latitudeinformation, estimated longitude information, or geotagging typeinformation.
 3. The method of claim 1, wherein deriving the candidategroup of estimated locations based on network measurement data duringthe static period comprises selecting network measurement datacomprising estimated location information, based on geotagging typeinformation associated with the network measurement data during thestatic period.
 4. The method of claim 1, further comprising merging, bythe network equipment, a consecutive static period with the staticperiod, wherein the estimated location represents the location of theuser equipment during the static period and the consecutive staticperiod.
 5. The method of claim 4, further comprising reclassifying, bythe network equipment, a moving period between the static period and theconsecutive static period, resulting in a reclassified static period,and merging, by the network equipment, the reclassified static periodwith the static period and the consecutive static period.
 6. The methodof claim 1, wherein identifying the static period comprises identifyinga recurring pattern of network measurement data within the time sequenceof network measurement data.
 7. The method of claim 6, wherein therecurring pattern of network measurement data comprises networkmeasurement data that repeats within a predetermined time interval. 8.The method of claim 1, wherein assigning the reliability scores toestimated locations of the candidate group of estimated locationscomprises comparing first distance information derived from the networkmeasurement data during the static period with second distanceinformation derived from the network measurement data during the staticperiod.
 9. The method of claim 1, wherein assigning the reliabilityscores to estimated locations of the candidate group of estimatedlocations comprises comparing first azimuth information derived from thenetwork measurement data during the static period with second azimuthinformation derived from the network measurement data during the staticperiod.
 10. Network equipment, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: obtaining atime sequence of network measurement data associated with a userequipment; identifying, within the time sequence of network measurementdata, a static period in which the user equipment remained static;merging a consecutive static period with the static period;reclassifying a moving period between the static period and theconsecutive static period, resulting in a reclassified static period;merging the reclassified static period with the static period and theconsecutive static period, resulting in a merged static period; andassigning an estimated location to represent a location of the userequipment during the merged static period.
 11. The network equipment ofclaim 10, wherein the operations further comprise deriving, based onnetwork measurement data during the merged static period, a candidategroup of estimated locations, and selecting the estimated location fromthe candidate group of estimated locations.
 12. The network equipment ofclaim 11, wherein the operations further comprise assigning, based onthe network measurement data during the merged static period,reliability scores to estimated locations of the candidate group ofestimated locations, and wherein selecting the estimated location isbased on the reliability scores.
 13. The network equipment of claim 12,wherein assigning the reliability scores to estimated locations of thecandidate group of estimated locations comprises comparing firstdistance information derived from the network measurement data duringthe static period with second distance information derived from thenetwork measurement data during the static period.
 14. The networkequipment of claim 10, wherein identifying the static period comprisesidentifying a recurring pattern of network measurement data within thetime sequence of network measurement data.
 15. The network equipment ofclaim 14, wherein the recurring pattern of network measurement datacomprises network measurement data that repeats within a predeterminedtime interval.
 16. A non-transitory machine-readable medium, comprisingexecutable instructions that, when executed by a processor, facilitateperformance of operations, comprising: deriving, based on networkmeasurement data from a stationary period of a mobile device, candidateestimated locations of the mobile device; assigning, based on thenetwork measurement data, reliability scores to estimated locations ofthe candidate estimated locations, wherein assigning the reliabilityscores to the estimated locations of the candidate estimated locationscomprises comparing first distance information derived from the networkmeasurement data during the stationary period with second distanceinformation derived from the network measurement data during thestationary period; selecting, based on the reliability scores, anestimated location of the candidate estimated locations; and assigningthe estimated location to represent a location of the mobile deviceduring the stationary period.
 17. The non-transitory machine-readablemedium of claim 16, wherein a smaller difference between the firstdistance information and the second distance information corresponds toa higher reliability score.
 18. The non-transitory machine-readablemedium of claim 16, wherein assigning the reliability scores to theestimated locations of the candidate estimated locations comprisescomparing first azimuth information derived from the network measurementdata during the stationary period with second azimuth informationderived from the network measurement data during the stationary period.19. The non-transitory machine-readable medium of claim 18, wherein asmaller difference between the first azimuth information and the secondazimuth information corresponds to a higher reliability score.
 20. Thenon-transitory machine-readable medium of claim 16, wherein deriving thecandidate estimated locations comprises selecting location informationfrom location information included in the network measurement data.